# Measuring uncertainty in human visual segmentation

arXiv: https://arxiv.org/abs/2301.07807 · HTML: https://mlsanity.com/paper/2301.07807/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 61 references that are in the map.
- First posted on arXiv 18 Jan 2023.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Image Segmentation Using Deep Learning: A Survey](https://mlsanity.com/paper/2001.05566/) (2020)
- [Markerless tracking of user-defined features with deep learning](https://mlsanity.com/paper/1804.03142/) (2018)
- [Learning long-range spatial dependencies with horizontal gated-recurrent units](https://mlsanity.com/paper/1805.08315/) (2018)
- [Disentangling neural mechanisms for perceptual grouping](https://mlsanity.com/paper/1906.01558/) (2019)
- [Convolutional Oriented Boundaries](https://mlsanity.com/paper/1608.02755/) (2016)
- [Unsupervised Segmentation in Real-World Images via Spelke Object Inference](https://mlsanity.com/paper/2205.08515/) (2022)
- [Object Contour and Edge Detection with RefineContourNet](https://mlsanity.com/paper/1904.13353/) (2019)
- [Bayesian Modeling of Motion Perception using Dynamical Stochastic Textures](https://mlsanity.com/paper/1611.01390/) (2016)
- [Flexibly Regularized Mixture Models and Application to Image Segmentation](https://mlsanity.com/paper/1905.10629/) (2019)

## Built on by (most cited first, 3 of 3)

- [3SHNet: Boosting Image-Sentence Retrieval via Visual Semantic-Spatial Self-Highlighting](https://mlsanity.com/paper/2404.17273/) (2024)
- [AdapToPASS: Ambiguity-aware Adaptive Spherical Transformer for Panoramic Semantic Segmentation](https://mlsanity.com/paper/2608.29081/) (2026)
- [Ideal Observer for Segmentation of Dead Leaves Images](https://mlsanity.com/paper/2512.05539/) (2025)

## Abstract (arXiv)

Segmenting visual stimuli into distinct groups of features and visual objects is central to visual function. Classical psychophysical methods have helped uncover many rules of human perceptual segmentation, and recent progress in machine learning has produced successful algorithms. Yet, the computational logic of human segmentation remains unclear, partially because we lack well-controlled paradigms to measure perceptual segmentation maps and compare models quantitatively. Here we propose a new, integrated approach: given an image, we measure multiple pixel-based same--different judgments and perform model--based reconstruction of the underlying segmentation map. The reconstruction is robust to several experimental manipulations and captures the variability of individual participants. We demonstrate the validity of the approach on human segmentation of natural images and composite textures. We show that image uncertainty affects measured human variability, and it influences how participants weigh different visual features. Because any putative segmentation algorithm can be inserted to perform the reconstruction, our paradigm affords quantitative tests of theories of perception as well as new benchmarks for segmentation algorithms.